{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import 必要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dpath = './data/'\n",
    "\n",
    "train = pd.read_csv(dpath+'RentListingInquries_FE_train.csv')\n",
    "# train = train.iloc[:100,:]\n",
    "y_train = train['interest_level']\n",
    "X_train = train.drop('interest_level',axis=1)\n",
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看看样本分布，看看是否均衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d3c9da0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kdcZQkSR1xlCRJHXGUJEkdcZQkSR1xlCRJHVmZL/8KI3TDy//F+MewlHhNR+7b9xD0GHG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\nktQZQ0WS1BlDRZLUGUNFktSZ/w9lZsRU/EFK2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cccfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分布不均匀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare cross validation \n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "kfold = list(kfold.split(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use cv built in xgboost, cross validate n_estimators quickly\n",
    "def modelfit(alg, X_train, y_train, cv_folds=None, early_stopping_rounds=10):\n",
    "    xgb_param = alg.get_xgb_params()\n",
    "    xgb_param['num_class'] = 3\n",
    "    \n",
    "    #直接调用xgboost，而非sklarn的wrapper类\n",
    "    xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "             metrics='mlogloss', early_stopping_rounds=early_stopping_rounds)\n",
    "  \n",
    "    cvresult.to_csv('1_nestimators.csv', index_label = 'n_estimators')\n",
    "    \n",
    "    #最佳参数n_estimators\n",
    "    n_estimators = cvresult.shape[0]\n",
    "    \n",
    "    # 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "    alg.set_params(n_estimators = n_estimators)\n",
    "    alg.fit(X_train, y_train, eval_metric='mlogloss')\n",
    "   ja\n",
    "\n",
    "   #Print model report:\n",
    "    print (\"logloss of train :\" )\n",
    "    print (logloss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of train :\n",
      "0.494944930518\n"
     ]
    }
   ],
   "source": [
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=6,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb1, X_train, y_train, cv_folds = kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6WhGRfJWzUHD3FHAZcC+wELjd3V8ys2vM7KxotpOBRWa2GBgLXJurevplRlPpBEa06wI2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\nK+KDx09h1qRaZk2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13eb0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "        \n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "        \n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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njtysxyjiNaRtVUTQ1d5EU+NYj9wzN9AjN9k8t2rQ+v2Hnj86z2117xCr+sos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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13eb08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[100:]\n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(100,cvresult.shape[0]+100)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
